As promised in a previous blog, I am going to talk about the advantages of linking more than one analytical technology to produce complementary particle characterization data. In this blog I shall consider the potential of linking laser diffraction analysis with image analysis data.
Complementary technologies deliver choice
Both laser diffraction and automated image analysis are powerful, primary analytical techniques for predicting product quality during manufacturing. As my colleage Ulf Willén and I discussed in a recent article; Particulate Analysis For Commerical Success, the ability of these techniques to be used together to provide relevant data can be a significant aid during product development.
When used to measure particle size, the results obtained with each method often compare closely enough to allow one technique to be used as a reference for the other, although this does become difficult when measuring high aspect ratio particles such as needles. This means that analysts can switch between methods at different points of the development cycle, to whichever will deliver the most benefit under those particular circumstances. For instance, it may be that during the initial development of a product the amount of sample available for testing is small. Use of image analysis methods may be advantageous in this case, as a large amount of size and shape information can be obtained based on a very small sample. However, once in production, using a technique which samples a greater mass of material may aid with obtaining results which are representative of the bulk material. In this situation laser diffraction may be considered more relevant.
One situation where the techniques are commonly used together is in support of method development for laser diffraction measurements. Using image analysis you can verify the choice of optical properties used in analysing the laser diffraction scattering data. You can also verify the state of dispersion achieved during a laser diffraction measurement, as image analysis can be used to differentiate between undispersed agglomerates and large primary particles.
Anyone intending to compare data produced by imaging and laser diffraction must first consider several issues including:
- The reported parameter – what single figure does the method use to represent particle size?
- Data presentation – are the data presented as number or volume/mass based distributions?
- Sample orientation – how is this affected by particle shape and how does it impact measurement?
- Sample dispersion – is the material present as agglomerates or individual particles?
While laser diffraction is an ensemble particle sizing technique that reports equivalent spherical diameter data on a volume or mass basis, image analysis is a counting technique that presents an array of size and shape parameters as number-based distributions. Understanding the differences between these size distribution representations is key to allowing comparisons to be made between the techniques. However, it can also help in understanding the strengths of each technique. The table below, taken from Malvern’s website, provides a quick reference for how the Malvern’s Morphologi G3 and Mastersizer 2000 system compare:
|Morphologi G3 imaging system||Mastersizer 2000 laser diffraction analyzer|
|Produces number distributions||Produces volume distributions
|High sensitivity to small particles|| High sensitivity to over-sized material
|Samples a small amount of material|| Samples a large amount of material
|Delivers specific particle properties|| Bulk material properties
|High resolution and sensitivity|| Robust, reproducible measurements
|Presents detailed sample information|| Provides rapid particle characterization
|Resolves Precise Morphological Information|| Resolves Broad Size Distributions
|Research and diagnostic tool|| Routine sample analysis tool
Reaping the rewards
Understanding the principles underpinning each technique, along with the form in which data are presented, is an important aspect of linking technologies. Through detailed consideration of each method, analysts can both optimize measurement processes and achieve even greater understanding of the samples they are characterizing.